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XGBoost Model With CMR Features for Prognostic Assessment in Patients With ST-Segment Elevation Myocardial Infarction
Yizhi Zhang1, Jiyuan Chen1, Zhiguo Zou1
1Department of Cardiology, Shanghai Renji Hospital, School of Medicine, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Insights
Predicting long-term adverse events after ST-segment elevation myocardial infarction (STEMI) is crucial. An XGBoost model using clinical and cardiac magnetic resonance (CMR) imaging data accurately forecasts these events, identifying microvascular obstruction as a key predictor.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Accurate prognosis for ST-segment elevation myocardial infarction (STEMI) is vital for clinical decision-making.
- Existing models may not fully leverage advanced imaging and machine learning techniques.
Purpose of the Study:
- To develop predictive models for long-term major adverse cardiac and cerebrovascular events (MACCEs) in STEMI patients.
- To integrate demographic, clinical, and cardiac magnetic resonance (CMR) imaging data for enhanced prediction.
- To compare the performance of different machine learning algorithms in forecasting MACCEs.
Main Methods:
- Development of four predictive models (naive Bayes, logistic regression, k-nearest neighbors, XGBoost) using 24 variables.
- Utilized CMR imaging data acquired within 1 week and 1 month post-primary percutaneous coronary intervention.
- Assessed model interpretability using Shapley values.
Main Results:
- The XGBoost model exhibited superior predictive performance for long-term MACCEs.
- Key CMR predictors included microvascular obstruction, left ventricular ejection fraction recovery, and infarct size.
- Clinical factors like Killip class, BMI, and age also significantly influenced predictions.
Conclusions:
- An XGBoost model integrating clinical and CMR data effectively predicts long-term MACCEs in STEMI patients.
- Microvascular obstruction identified via CMR is a critical prognostic factor.
Background:
Accurate prognostic models for ST-segment elevation myocardial infarction (STEMI) are essential to guide clinical practice.
Objectives:
This study sought to construct predictive models integrating demographic, clinical, and cardiac magnetic resonance (CMR) imaging variables to forecast long-term major adverse cardiac and cerebrovascular events (MACCEs).
Methods:
Patients with STEMI underwent CMR imaging within 1 week and 1 month after primary percutaneous coronary intervention. Twenty-four demographic, clinical, and CMR variables were used to construct 4 predictive models (naive Bayes, logistic regression, k-nearest neighbors, and XGBoost) for forecasting MACCEs during long-term follow-up. Model interpretability was assessed using Shapley values.
Results:
A total of 483 patients were included (median age: 59.6 years, IQR: 54.0-65.0 years; median follow-up: 89.3 months; IQR: 60.3-115.4 months). During follow-up, 98 of 483 patients (20.3%) experienced MACCEs. The XGBoost model demonstrated superior predictive performance compared with the other approaches. Key CMR predictors included microvascular obstruction, left ventricular ejection fraction recovery, infarct size, intramyocardial hemorrhage, infarct core T1, and remote myocardium T1. Among clinical features, Killip class, body mass index, and age were most influential. Remote myocardium T1 was inversely correlated with left ventricular ejection fraction recovery at 1 month after percutaneous coronary intervention (R = -0.34; 95% CI: -0.43 to -0.27; P < 0.01).
Conclusions:
An XGBoost model integrating clinical and CMR features effectively predicted long-term MACCEs in patients with STEMI. Microvascular obstruction emerged as the most important CMR-based prognostic factor.
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